User-specific media playlists

ABSTRACT

A media recommendation system may score media items according to user recommendations, popularity, and/or recency. The scores may be weighted to produce an overall score for each media item. Media items may be added to a pool for a specific user, from which media items are selected for playback. The contents of the pool may be modified based upon user feedback and other data. The pool may be modified dynamically and/or in real time as media items are consumed or rated by the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to, and the benefit of, of U.S.Provisional Application Ser. No. 62/083,789, filed Nov. 24, 2014 andU.S. Provisional Application Ser. No. 62/083,840, filed Nov. 24, 2014,the disclosure of each of which is incorporated by reference in itsentirety.

BACKGROUND

A media recommendation service selects a user-specific subset of mediaitems from the universe of available media items that the servicedetermines may be enjoyed by the user. The selected media item can beprovided to the user through a network to be rendered on a device of theuser, such as a mobile device. A media item can be a song, video,animation, document or other media entity. When a user of the systembegins a new session, the user can be associated with the subset of therecommended media items corresponding to the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosed subject matter, are incorporated in andconstitute a part of this specification. The drawings also illustrateembodiments of the disclosed subject matter and together with thedetailed description serve to explain the principles of embodiments ofthe disclosed subject matter. No attempt is made to show structuraldetails in more detail than may be necessary for a fundamentalunderstanding of the disclosed subject matter and various ways in whichit may be practiced.

FIG. 1 shows an example of a computing device suitable for implementingembodiments of the disclosed subject matter.

FIG. 2 shows a network configuration according to an embodiment of thedisclosed subject matter.

FIGS. 3A-3B illustrate the number of total times a user has listened toa song in an example music recommendation system according to anembodiment.

FIG. 4 shows an example process that may be used to generate auser-specific pool according to an embodiment.

FIG. 5 shows an example process in which a pool is modified dynamicallybased on user feedback of media items in the pool according to anembodiment.

FIG. 6 shows an example of a computerized recommendation systemaccording to an embodiment.

DETAILED DESCRIPTION

According to embodiments of the disclosed subject matter, a media itemrecommendation system may score a media item according to userrecommendations, popularity and/or recency. Such scores may be weightedand combined to produce an overall score for an item. The system mayselect a media item for inclusion on a playlist, i.e., a list of mediaitems that will be played for or otherwise presented to a user, forfurther processing or for recommendation to a user based upon one ormore of the item's scores.

As disclosed herein, a media recommendation service can select a subsetof media items that a given user may enjoy, and organize them into a“pool.” A pool can be an ordered collection of media items, potentiallysongs, videos, animations, documents or other media entities. Morespecifically, as used herein a “pool” refers to the media items fromwhich a recommendation and media playback system will draw whenpresenting media items to a particular user. A pool may be distinguishedfrom a conventional playlist in that the pool may be modifieddynamically, based upon the user's interaction with items selected to beplayed from the pool, whereas a recommendation playlist typically is notmodified once created by a recommendation system. However, in someembodiments, a media recommendation and playback system as disclosedherein may present the items selected from a pool in a playlist format,thereby allowing a user to review items that have been played and, insome embodiments, to see items that may be upcoming for playback fromthe pool. Such a playlist interface may be generated dynamically and, insome embodiments, may not present upcoming media items to the user, asthe next item to be played may not be selected prior to being initiallyplayed for the user. Thus, as used herein, a “playlist” generated by amedia recommendation system refers to the list of media items selectedby the recommendation system from a pool of potential media items, inthe order in which they are presented to the user.

When a user of the system begins a new session, the system can associatethat user with a pool corresponding to the user. That is, a mediarecommendation system may include, or be able to provide access to, alarge number of media items such as songs, videos, or the like. For eachuser that accesses the system, a pool of media items may be created,which includes a subset of the media items available in the system as awhole. For example, a pool may be represented by an ordered list ofmedia items, all of which are selected from among the total media itemsavailable in the system. Further details regarding the construction,modification, and use of a pool are provided below. A playlist also maybe generated for a user, with items in the playlist being selected fromamong the pool of media items created for the user. A playlist also maybe represented as an ordered list of media items, with the orderrepresenting the order in which the items will be played for the user.

The system can choose an item to include in the pool based on severalcriteria. For example, the system can include “user-recommended”recommendations, “popular” recommendations, and “recent” recommendationsin the pool. “User-recommended” items can be those that are recommendedfor the user based on the system's proprietary recommendation enginereceiving actual user consumption habits as input.

For example, the system can receive as an input the identity of an itemthat the user has consumed (listened to, watched, read, etc.), anindication that the user has explicitly “liked” an item (e.g., the userhas selected an onscreen button indicating the user's approval of theitem), an indication of a high rating or positive review of the item bythe user, etc. The system can identify similar items to the consumed orrated item. The similarity indicator of a candidate can have a magnitudeindicating the degree of similarity with a consumed/rated item. In animplementation, the system can select candidate items for inclusion inthe pool if the candidate items have a similarity indicator equal to orgreater than a similarity threshold. In another implementation, thesystem can select the N most similar candidate items for inclusion inthe pool, where N is an integer. The same and other criteria can be usedto select candidate items not for immediate inclusion in the pool, butto be subject to further filtering based on other criteria.

“Popular” items can be the result of aggregating the actual consumptionactivity amongst the entire system's network of users and/or the user'sspecific network of friends, and/or the user's personal consumptionhistory. Each item can be assigned a numeric value corresponding to apopularity score in each of these categories. The items may be arrangedinto an ordered list for each category, from most to least popular forthat category. A specific weighting can be applied to the popularityscore for each category (“category popularity”). For example, personalconsumption history can be assigned a weight of 0.6, the user's networkof friends can be weighted 0.25 and the actual consumption history canbe weighted 0.15. These can be combined into an overall popularity scoreby summing the weighted popularity score for each item in each category.This can produce a list of items that can be ordered from overall mostto least popular.

In some embodiments, an item may be selected for inclusion in a set ofitems for the pool or for further processing based on the position ofthe item in overall popularity or category popularity. For example, anordered list of items from most to least popular can be divided intoquintiles, the first (top) quintile containing the top fifth mostpopular items and the fifth (bottom) quintile containing the bottomfifth (least popular) items. The number of items selected from anordered category or overall popularity list for the pool or for furtherprocessing can be adjusted. For example, the system can select 80% ofitems from the top quintile, 15% of items from the middle (secondthrough fourth) quintiles and 5% of items from the bottom quintile. Theitems can be selected randomly, or in accordance with another criteria.For example, the system can select the 80 items with the highestuser-recommended scores from the top quintile, the 15 items with thehighest user-recommended scores from the middle quintiles and the 5items with the highest user-recommended scores from the bottom quintile.Any combination of criteria can be applied in this way.

“Recent” items can be chosen based on the user's actual recent externalconsumption activity, meaning items consumed outside of the systemwithin a given recent timeframe, such as the last hour, the last day,the last week, etc. The result can be an ordered list from most to leastrecently consumed items. An item's position on the ordered list can bealtered in accordance with other criteria, such as the number of timesthe user has consumed the item. For example, an item that was consumedeight times over the past week by the user can be promoted ahead (up thelist) over another item that the user has consumed once over the sameperiod. Likewise, a frequently consumed item may be promoted above amore recently consumed item that was consumed a fewer number of times bythe user.

A combination of these and potentially other recommendation results canbe combined and filtering can be applied for higher quality results. Forexample, an ordered list generated by any of these techniques, eitheralone or in combination, can be further processed by taking into accountthe number of friends of the user (or others) who consumed the samemedia item and identifying items that have not been recently consumed inthe system by the user. Such an item could be desirable to the user andcan be selected by the system to recommend to the user.

In an embodiment, a profile vector may be created for each user of thesystem as described in further detail herein, which can be used todetermine items that will be used as a seed for the user, recommended toa user, placed in a user's pool, or otherwise considered forpresentation to the user. For example, a modified CollaborativeFiltering approach may be used.

A Collaborative Filtering model constructs a user vector {right arrowover (v)}_(u) and an item vector {right arrow over (v)}_(i) for allusers u and items i in the system, respectively. An “item” in thiscontext may be an item such as a song, film, video, or the like, or acreator of the item, such as an artist. The closer the orientation agiven user vector is to an item vector, the greater the likelihood thatthe user is interested in the item and, therefore, that the item shouldbe considered for inclusion in the user's pool or presentation to theuser. The distance between the two vectors can be determined as the dotproduct of the vectors.

The training process of the Collaborative Filtering model may beconstructed to minimize

$\sum\limits_{i}( {M_{u,s} - {{\overset{arrow}{v}}_{u} \cdot {\overset{arrow}{v}}_{i}}} )^{2}$

With M being a matrix of users (rows) and media items (columns), and thedot product being close, but not equal, to the original matrix elementM_(u,i). An element of the matrix M_(u,i) indicates the number of uniquetimes a user u has consumed the media item i. A weight may be includedin the value, for example, to give particular weight to a user'spositive interaction with a media item. As a specific example, a “like”or other positive interaction may be considered as multiple uniqueconsumptions. If the user u has not consumed the item i, then the matrixelement M_(u,i) value is 0. Because users typically consume a relativelysmall percentage of the total number of media items available, Mtypically is very sparse. A similar model may be trained using a matrixthat includes users vs. artists (instead of individual media items). Itis not possible to have a perfect match between the matrix element andthe dot product. However, this is advantageous because it allows thematrix to be filled with non-zero weights (the dot product between theuser and item vectors), to result in a less sparse matrix. This allowsfor the prediction of items that a user may be interested in, even ifthe user has never previously interacted with the particular item(s).More specifically, it allows for the creation of a ranked list of itemsfor each user, as previously described. This also may be useful fortesting purposes as disclosed herein.

In an embodiment, Collaborative Filtering may be used in the context oflogistic regression to provide a probability that a given user consumesa given item i as

$P_{u,i} = \frac{e^{{\overset{arrow}{v}}_{u} \cdot {\overset{arrow}{v}}_{i}}}{e^{{\overset{arrow}{v}}_{u} \cdot {\overset{arrow}{v}}_{i}} + 1}$

where P_(u,i) is the probability of the user u consuming the item i. Abias term also may be added to the exponent terms to account forpopularity bias of specific users and/or items. The model may be trainedby alternately modifying the user and items vectors and minimizing errorvia a gradient descent, by calculating partial derivatives with variablestep sizes. Such a calculation may allow for improved operation of animplementing computer system, since it can be parallelized for moreefficient operation by mapping portions of the calculation to multipleprocessors.

Choosing an item for a playlist from the current pool can dictate how auser's session will begin and how the rest of the pool is ordered, basedon similarity to the chosen item. The initial item to be played on theplaylist can be based on an item recently consumed by the user, as thisinvokes a sense of familiarity and trust. On the other hand, if thesystem always started with a recently consumed item, it may make theexperience feel redundant for the user. In that case, an item may bechosen based upon a “user-recommended” score. If there are no“user-recommended” items (for example in the case of a new user), thesystem may choose an item based upon a high popularity score. Once anitem is chosen, item-to-item similarity scores can be calculated againstthe remaining items in the pool, which can then be rearranged in theirorder of similarity to the already-chosen item.

However, it is common for recommendation systems and techniques tosuffer from the “cold start problem,” i.e., that it may be difficult toprovide a high-quality recommendation if it is not known what items auser has previously consumed. Typically, this problem is addressed byusing various offline training techniques and a sufficiently-large dataset on a diverse set of users. In contrast, embodiments disclosed hereinmay avoid the need for such training and/or large datasets by creating areal-time, “online” profile of the user.

For example, the Collaborative Filtering vectors of all items consumedby a user may be added, as the user is consuming items, and theresultant vector may be normalized to a unit normalization. Thisprovides an orientation of the user that allows the system toimmediately provide at least a relatively basic level ofpersonalization. The online profile also may be used to update userswith offline profiles (i.e., users not concurrently accessing the mediasystem). Additions to a profile may be made inversely proportional tothe total number of times the user has consumed items, so as not toperturb established profiles. Users with no consumption history, i.e.,users who are entirely new to the system or for whom consumption data isotherwise not available, may be recommended items based onpopularity-based filtering and/or demographic-based filtering, asdisclosed herein.

As previously described, embodiments disclosed herein may create a“pool” of media items or other items for potential consumption from auser from among the items available within a recommendation system.Typically the number of items in the pool is much smaller than thenumber of items available within the system as a whole. For example, amusic recommendation system may include tens of thousands of individualsongs, or more, whereas a pool for an individual user may include only100 or fewer individual songs. The specific items in the pool also maychange more dynamically than the items in the system as a whole.

In an embodiment, when an initial seed item is selected for a specificuser, an initial pool of items that may follow the seed item may becreated using Context-Based Filtering as disclosed herein. For example,a Context-Based Filtering system may represent items within the system,or within a user's pool, as vectors, as previously described. As aspecific example, the word2vec algorithm, which conventionally is usedto represent individual words or phrases as vectors, may be used torepresent media items as vectors. Each media item, such as a song, maybe considered a word, and media items played consecutively in a playlistas words following one another. Session windows may be defined basedupon typical consumption patterns for media items. The session windowsmay be used as the context windows considered by the word2vec algorithm.

Relatively naïve implementations of Collaborative Filtering andContext-Based Filtering as disclosed herein may be inefficient for anumber of reasons. For example, when searching for relevant items toplace in a user's pool or a particular playlist for a user, the dotproduct may be computed for every item against an input vector x ofinterest. However, typically only the top matches to x will be ofinterest, so computing the dot product of x and every item available ina pool or, to an even greater degree, within the system as a whole, maybe inefficient. Collaborative Filtering models also may be relativelyaccurate at detecting large-scale structures in datasets, but relativelypoor at detecting strong associations in smaller datasets. To addressthese potential inefficiencies, nearest neighbor items may be used thathash item vectors into buckets. Partitioning items and subsequenthashing allows for a relatively fast calculation of nearest neighboritems, by increasing the lookup time using hashes that are based onpre-computed nearest neighbors.

As previously described, embodiments disclosed herein may use varioustechniques to select the initial seed items that are used to generate apool of items for a particular user. It may be desirable for seedoptions that are used and/or presented to the user as options forinitial seeds, to be both diverse and personalized to the user. Withregard to media items, conventional genre labels often fail to capturethe ways in which people actually consume media. For example, usersoften may listen to popular hip hop music alongside popular electronicmusic. Instead of relying upon existing genres and categories, it may beuseful to generate categories and/or clusters of media items based uponactual user consumption. Thus, some embodiments may constructconsumption-generated categories. Other categories that may be appliedto media items include “trending” and “emerging”. Alternatively or inaddition, media items may be categorized as recently consumed and/orrecently liked, where a user or users has/have consumed or “liked” amedia item within a threshold amount of time.

As previously described, each media item and/or artist may berepresented by a distinct vector within a recommendation system. Thecloser the vector representation of one item or artist is to another,the more similar the artists or items are. As described in furtherdetail below, the number of times a particular media item is consumedtypically is power-law distributed, with a small percentage of artistsdominating the number of consumptions. Thus, a collection of topartists' artist-space vectors may be aggregated, and a two-dimensionalreduction on the artist vectors may be performed, such as byt-distributed Stochastic Neighbor Embedding (t-SNE). Such a techniquemay be particularly effective in generating well-separated clusters. Theresult is a two-dimensional vector for each artist. Clusters of artistvectors then may be created, such as using Dirichlet process mixturemodels. More generally, any nonparametric process may be used. Typicallythe process will not require a predetermined number of clusters, and/ormay determine an optimal number of clusters automatically. In otherembodiments, any clustering algorithm may be used.

After artist clusters are created, a Gaussian Mixture Model may be usedto create discrete categories for each artist, which are based solely onactual consumption by users. In addition, a mixture model may generatethe probability of any artist belonging to any of the generatedcategories, including those that are not the closest match. The processmay allow for partial membership in clusters, and consumption-drivencategories that do not depend upon rigid, predefined genres. This isbecause a Gaussian Mixture Model presumes that artist vectors aregenerated from a mixture of Gaussian distributions with various meansand variances. The closer an artist vector is to the mean of aparticular category's Gaussian, the better a match it is for thatcategory. Similarly, an artist vector is penalized for being fartherfrom the mean, for a smaller variance of a particular Gaussian. Theprobability for a particular artist to belong to a particular cluster iis

$w_{i} = e^{- \frac{{({\overset{arrow}{x} \cdot \overset{arrow}{u}})}^{2}}{2\sigma_{i}^{2}}}$

where w_(i) is the weight for cluster i, x is the location of the tSNEprojected artist vector under consideration, u_(i) is the center or meanvector of cluster i, and σ_(i) is the standard deviation of cluster i.

For each category, the artists may be stored by their respective matchto the category, including artists on the periphery, i.e., that are notnear the center of the category. A random number may be used to draw anappropriate assignment of artists or media items to a particular pool,which may be weighted proportionally to the weight of the artist in thecategory.

In some embodiments, a category that includes all artists that are notincluded on a “top artists” list may be added artificially. This “notpopular” category may be used to generate suggestions for users thatdesire less-popular media items. The “top artists” may be determinedbased upon the power-law distribution previously described and asdisclosed in further detail below. For example, all artists past acritical peak as shown and described may be considered “not popular”artists.

The weight of each category may be stored for each user in arecommendation system, for example in a N-dimensional array of weights(w₁, w₂, . . . , w_(N)) representing a sampling of all categories by theuser including the “not popular” category. Each user categorypreferences, as indicated by the user and/or as determined automaticallyby the system, may be stored in a Dirichlet distribution. The Dirichletdistribution is the conjugate prior of the multinomial distribution.Weights may be initialized with the weights generated by all other usersas previously disclosed, and the overall number of members with thetypical standard deviation for a user as determined across all users inthe system. This also may be used as a Bayseian prior. As a userconsumes media items, the weighting of the user's Dirichlet distributionmay be updated to reflect the user's preference (as determined by plays,likes, dislikes, and skips). This may provide seemingly-seamlesscategory personalization to each user.

In an embodiment, multiple seeds may be presented as options for theuser to begin consuming media items. For example, in a musicrecommendation and playback system, multiple initial songs may bepresented to the user. When the user selects a particular song, aplaylist may be generated and/or accessed, where the selected song isthe seed for the pool from which items on the playlist are drawn. Thepool may then be updated and/or items added to or removed from theplaylist based upon the user's consumption of items in the playlist, aspreviously disclosed. For example, the following types of seed mediaitems and/or artists may be presented as seed options to the user:

-   -   Trending—the top-performing media items or artists within a        recent time interval. For example, the media items or artists        that have been played by all users or a group of users within        the system over the past hour, day, week, or the like may be        included.    -   Emerging—relatively less well-known items/artists with a        relatively high consumption count. The consumption count may be        determined over a particular time interval. For example, less        popular media items that have been consumed at a higher rate        than other less popular media items within the last week may be        included.    -   Recent—items/artists that have been consumed by the user from a        source external to the recommendation system.    -   Liked—items/artists that have been explicitly “liked” or        otherwise indicated for approval by the user, either within the        recommendation system or an associated system, such as an        external social network that the system can access.    -   Selected—items/artists specifically curated by staff members of        a media recommendation system, advisors, or the like.    -   Active—media items that similar or socially-connected users are        consuming at the moment. For example, if a user has a social        network connection to another user within the recommendation        system or within an external social network, and the connected        user is listening to a particular song, the song or the        associated artist may be presented as an “active” seed. In some        embodiments, an indication of the connected user or users from        which the seed has been drawn may be provided to the user.    -   Recommended—items/artists that are close or closest to the user,        based upon the profile vectors previously described herein.        For each type of seed, a specific seed item may be selected to        be presented to each user using any suitable technique. For        example, within a seed category, the particular item presented        to the user may be the item that is closest to the user's        profile vector. As another example, a particular item may be        selected randomly from within all items in the seed category.        Alternatively or in addition, the seed type may be presented to        the user for selection. For example, an interface may show the        user the specific media item and/or artist, as well as the seed        category the item/artist represents, such as “trending,”        “emerging,” or the like.

When a user selects a particular seed, a pool may be generated from theseed as previously described. The pool then may be updated based upon auser's interaction to media items played from a playlist generated fromitems selected from the pool, as previously disclosed.

As previously disclosed, in many cases the majority of users in a mediarecommendation system may have a relatively low consumption compared tothe most active users within the system. For example, FIGS. 3A-3Billustrate the number of total times a user has listened to a song in anexample music recommendation system as disclosed herein. As shown thenumber of “listens” are power law distributed, with a peak at a criticalnumber of songs.

In an embodiment, users with a relatively high consumption level, e.g.,before the peak in FIGS. 3A-3B, may be treated differently than thosewith a relatively low consumption level. For example, users with fewlistens, i.e., those who have consumed relatively fewer media itemswithin a recommendation system, an online profile vector may be createdfor the user as previously described. Although the user's vector may beupdated dynamically as previously described, the Collaborative Filteringprocess may not be applied to media items suggested to or added to apool or playlist of the user. Instead, demographic and/or popularityfiltering as disclosed herein may be used. This may prevent the userfrom receiving a large number of recommendations of media items that theuser may find unfamiliar, unexpected, or undesirable.

Other filtering techniques may be used in addition to or instead of thefiltering techniques previously disclosed. For example, media itemsplaced in a pool or playlist for a user may be selected or filteredbased upon the time of day. For example, the time at which a userconsumes, skips, likes, or dislikes a particular media item may berecorded and, after the user has interacted with a sufficient number ofmedia items, statistically significant preferences for the user basedupon the time of day may be determined. For example, a classifier may beused to determine favorable categories for a user based upon the time ofday. The classifier may be configured to apply the same categorizationtechniques previously described, but using the time of day as anattribute of previously-consumed media items. As another example, theBayesian updating approach previously disclosed may be used to determinecategory weights based upon the time of day. Similarly, a user'sdemographics may be used to determine statistically favorable categoriesin a similar fashion. As a specific example, gender, age, geographiclocation, or the like may be used to identify media items and categoryweights that may be appropriate to a particular demographic.

FIG. 4 shows an example process that may be used to generate auser-specific pool according to embodiments disclosed herein. At 410,one or more seed media items may be selected, using any of thetechniques previously disclosed herein. For example, in a musicrecommendation system, one or more seed songs may be selected. Multipleseeds may be selected and presented to the user as previously described.At 420, representations of the selected seeds may be provided to a user,such as via a selection interface. As an example, song or album coversfor the specific seed songs selected may be presented in an interface inwhich the user may select one of the seed songs to begin playback ofmedia items within the music recommendation system. At 430, the user'sselection is received. A pool of media items specific to the user may begenerated at 440. For example, at 450, songs having vectors within athreshold distance of the selected seed song may be added to the pool,as previously described. The pool may be sorted, for example, based onthe vector distance between each item in the pool and the seed song.After the seed song is played completely or is skipped or “disliked” bythe user, a subsequent item to play may be selected from the pool forplayback by the system as previously described.

As a user consumes items from a pool, the pool may be modified in anumber of ways. For example, the pool may be sorted based upon theprobability that each item should be provided to the user as the nextitem in a playlist, such as based on most recent consumption of the itemas defined by the item's Collaborative Filtering profile. As anotherexample, every time the user completely consumes and/or “likes” an item,similar items may be added to the pool. “Similar” items in this contextmay be those that are closest to the liked item based upon the items'Collaborative Filtering profiles, the item's nearest-neighbor matches int-SNE space as described herein, or based upon any other relativeranking of item similarity within the system. Similarly, if a user“dislikes” an item, such as via a “thumbs down” or similar interface,the most similar items may be removed from the pool, and replaced withother items selected using any technique disclosed herein. If a user“skips” an item, i.e., moves on to the next item in the existingplaylist but without actively “disliking” the item, the skipped item maybe removed from the pool. Alternatively or in addition, items similar tothe skipped item may be removed from the pool. The number of itemsremoved from the pool may be proportional to the number of skips inrecent history. Thus, if a user skips several items in a row, arelatively larger percentage of items may be removed from the pool, asthis may indicate that the user's current preferences have changed, orsimilar. When items from a pool are placed into the user's playlist,similar items may be placed together so as to provide for smoothtransitions between items. For example, in a music recommendationsystem, acoustically-similar songs may be placed adjacent within theplaylist, presuming that they are of comparable likelihood to bepresented at a particular point in the playlist, so as to allow forrelatively smooth acoustic transitions between items in the playlist.

More generally, a media item recommendation system as disclosed hereincan monitor a user's behavior and interaction with items in a pool,including receiving explicit feedback from the user. The informationobtained from such monitoring can be used by the system to modify andreorder the items available in a media item pool that corresponds to theuser.

In particular, the system can utilize input based on the user'sactivities to schedule which item to cue up next, such as which mediaitem to play next in a sequence of items played for the user. Types ofuser responses can be categorized generally as “positive feedback” or“negative feedback.” Positive feedback can indicate that the user likelyenjoys the item being played, and the system may determine thatadditional items with similar qualities should be prioritized and addedto the pool as the session continues. For example, similar media itemsas previously described can be added to the user's pool, or a poolassociated with a particular seed, as previously described. Negativefeedback can indicate that the user doesn't enjoy the item being playedor otherwise does not want to consume or finish consuming the mediaitem, and the system may remove similar items from the pool.

Specific examples of positive feedback may include: “play”, which canindicate that the user has chosen to play a media item and can bereceived around the time a user commences playing the item or around thetime a media recommendation system begins playing the item as a “next”item in cases where the system is configured to play a sequence of itemsfrom the user's pool; and “play full” or a “playthrough”, when a userplays through the entire duration of an item without explicitly likingor skipping the item; “like”, when a user explicitly likes an item.Examples of negative feedback include “dislike” when a user explicitlydislikes an item, and “skip”, when a user skips a currently-playingmedia item to move to the next item. In some embodiments, other types ofuser actions and/or feedback may be included as positive or negativefeedback. As a specific example, a user may choose to exit a musicrecommendation service during playback of a song; this may be considerednegative feedback or not considered as feedback. As another example, theuser may request album purchase availability or other information duringplayback of a song; this may be considered positive feedback or notconsidered as feedback.

Each time the user submits positive feedback, the system may or may notattempt to add one or more new items to the user's pool. The number ofnew items to add may depend on the type of feedback. For example, oneitem may be added for a “play” event, two items may be added for a “playfull” event, and six items may be added for a “like” event. The systemcan select an item to add to the pool based on the current item beingplayed/liked/etc., and other recent positive feedback. The feedback canbe considered within a specific threshold of recent activity. Forexample, the system can consider the ten most recently received items offeedback, such as the most recently consumed, liked, rated or revieweditems, which may or not be limited to the same user session. The systemmay assign a “smooth transition score multiplier” to each of these itemsof feedback. For example, such a multiplier may be based upon a basevalue (for example 0.85) raised to the power of the relative position ofthe previously “liked” item. For example, the base can be raised to thepower 0 for the current item, 1 for the previous item, two for the itembefore that, and so on. The score for a given item can be multiplied bythe smooth transition score multiplier.

After an item is added to the user's pool, the order of items in thepool can be readjusted in order of item similarity to the most recentlyplayed item, for smoothness. When searching for similar items torecommend, the system can have advance knowledge of which items toexclude. For example, the system can exclude from adding items thatalready exist in the user's pool, that the user has consumed within arecent timeframe, that are created or performed by artists that the userhas recently consumed, e.g., within a recent consumption time or itemthreshold. For example, items that the user has consumed within the lasttwenty minutes, items that the user has consumed within the ten mostrecently consumed items, etc.

For example, a user engaged in an audio session may listen to and “like”(provide a positive indication about the song by, say, selecting an iconon a display) songs “A”, “B”, and “C” consecutively in that session.When responding to the “like” positive feedback for song “C”, the systemmay add 6 songs to the user's pool in response to the explicit “like”action. The system can assign to current song “C” a “smooth transitionscore multiplier” of 0.8{circumflex over ( )}0, or 1.0. Song “B” canreceive a multiplier of 0.8{circumflex over ( )}1, or 0.85. Song “C” canreceive a multiplier of 0.8{circumflex over ( )}2, or ˜0.72. The systemcan find which songs are the most similar to song “C” and can multiplyall of their similarity scores by 1.0. The system can find which songsare the most similar to song “B” and can multiply their similaritiesscores by 0.85. Finally, the system can find which songs are the mostsimilar to song “A” and can multiply their similarity scores by 0.72.The sums of all of the song similarity scores can be aggregated and theresults can be sorted in descending order. The 6 top-scoring songs canbe selected and added to the user's pool. The result of this “positivefeedback” is that the pool now has 6 more songs that are likely mostlysimilar to C, but also a bit similar to B, and a bit less similar to A.

“Negative feedback” can occur in any of several forms of “skip”, whichcan include a “soft skip”, when a user skips an item, but only after agood portion of the item has already played, and a “hard skip”, when auser skips an item before that “soft skip” threshold. A skip can bedetermined to be soft or hard based on a soft skip threshold, which cancorrespond to a measure of how much of the song was listened to before askip indication was received from a user. The threshold can be inseconds, percentage of the song, bits or any other suitable measure.

When a “soft skip” occurs, the system can treat it as though the userjust didn't want to consume any more of that item and apply no negativefeedback. A “Hard skip” can be further analyzed into tiers based onrecent skipping activity. After a hard skip occurs, the system cansearch for other recent negative feedback within a specific threshold ofrecent activity. For example, the system can examine 10 recent itemsfrom within the same session. Based on the number of recent skips withinthat threshold, a “skip tier” can be designated. For example, 1 recentskip could imply “tier 0”, 2 recent skips could imply “tier 1”, 3 recentskips could imply “tier 2”, and 4 or more recent skips could imply “tier3”.

The system may interpret “tier 0” as meaning that a user simply didn'tlike that specific item, or maybe its artist, so the system may take noproactive action.

With higher tiers, the system may remove items from the user's pool, thenumber of which depends specifically on the tier assigned and thesimilarity of the items currently in the pool. Existing items in thepool can be compared for similarity to the item currently being skipped.The mean and standard deviation of these similarity scores can becalculated, and then a threshold can determined based on the tier. Forexample, for “tier 1”, the threshold might be 1.645 standard deviationsto the right of the mean, resulting in approximately 5% results fornormally distributed scores. For tier 2, the threshold may be 1.282standard deviations to the right of the mean, resulting in approximately10% results for normally distributed scores. For tier 3, the thresholdmight be 0.674 standard deviations to the right of the mean, resultingin approximately 25% results for normally distributed scores. Items thathave similarity scores that fall above that calculated threshold,meaning the ones most similar to the item that is currently beingskipped, can be removed from the pool, and new recommendations can madeto fill in the number of removed items.

The overall impact of the positive and negative dynamic feedback duringa playlisting session can be that items similar to those that the usereither implicitly or explicitly likes can be recommended more often, anditems similar to those that the user explicitly skips can be recommendedless often. Ongoing modification and reordering of the user's pool cancreate a compelling overall listening experience.

FIG. 5 shows an example process according to embodiments disclosedherein in which a pool is modified dynamically based on user feedbackrelated to media items in the pool. Steps 410-470 may be performed aspreviously described with respect to FIG. 4. At 510, the system mayreceive feedback related to a media item in the pool that has beenconsumed, partially consumed, or otherwise interacted with by the user.For example, a user may play a song through, provide positive feedbacksuch as a “like”, or provide negative feedback such as a “skip” or“dislike.” As previously described, upon receiving feedback the systemmay modify the contents of the pool associated with the user at 520. Forexample, if a user plays a song through completely without skipping orproviding other negative feedback, similar songs may be added to thepool. The similarity may be based upon a vector distance between theplayed-through song and the added song, or upon any other comparison asdisclosed herein. The pool may be re-sorted at 460 after addition of thenew media item, or playback may continue at 470 without re-sorting thepool.

In an embodiment, a dynamic state learner may be used to estimate anexpected state of an individual user at a particular time. This mayallow for more accurate recommendation of media items to the user, byidentifying media items that are “close to” the expected state. Forexample, using a vector representation of a media item as previouslydisclosed, media items having vectors that are close to the expectedstate may be recommended.

An expected state of a user at time t can be denoted as E_(t). The statethen may be updated according to

E _(t) =γE _(t−1)+Δ_(t)

Δ_(t)=s_(t) for a “listen” or other indication that a user has consumedthe media item completely, Δ_(t)=β(τ)s_(i) for a media item that isskipped at a time τ seconds into playback, and Δ_(t)=αs_(t) for a “like”or similar explicit positive feedback, where s_(t) is the state of themedia item consumed (or partially consumed) by the user; β(τ) is aweight assigned to a media item that has been consumed for τ seconds;and a is the weight assigned to a “like”. In general, β(τ) may benegative for relatively short times, and zero for longer times. Theweights may be determined by training a machine learning system orsimilar artificial learning system based on historical data. The modelmay be constructed to have a weak assumption that each state is aslightly altered version of an older state by use of the γ factor, inaddition to the correction term Δ_(t). The model may be separatelytrained to apply at different points, or separate models may be trainedfor different points in operation of a recommendation system. Forexample, separate training or separate models may be used during aplayback session; when a user first selects a media item; and when auser moves between media items, such as during a transition followingconsumption of a media item or when a user skips a media item.

FIG. 6 shows an example of a computerized recommendation system asdisclosed herein. The system may be implemented on one or more computersystems, including servers, groups of servers, “cloud” architecture, orthe like, examples of which are provided with respect to FIGS. 1-2. Eachcomponent described may be implemented on a separate computer orcomputer system, or multiple components may be implemented on a commoncomputer or computer system. The system may provide an event ingestioninterface 610 to receive, for example, user feedback related to mediaitems such as “likes”, “listens”, and so on. The interface 610 mayreceive, sanitize, and denormalize such data for use in the system.Event data and other data used by the system may be stored in one ormore denormalized and/or relational databases 620, 630.

Data stored by the system may be used for model training, such as in acomputer learning system 640. For example, a profiler as disclosedherein may be implemented by the model training system 640. Such aprofiler may include the artist and/or item Collaborative Filteringmodels 642, 643, respectively, as disclosed herein; artist and/or itemnearest-neighbor models 644, 645, respectively, as disclosed herein;artist tSNE categorization 646 as disclosed herein; and/or user and/orcategory 648 sampling as disclosed herein. A context profiler system 650may generate profile vectors as disclosed herein, such as by usingword2vec, RNN, acoustic models, and/or other models, including thosedescribed herein.

As disclosed herein, the models trained by the model training system 640may be used to generate recommendations 670 for one or more users, whichcan be presented to the users via APIs and/or other interfaces 660. Forexample, in a music recommendation system, recommendations of songs theuser may enjoy may be provided via a web interface, a mobile interface,or any other suitable user interface 660. Access to recommendations alsomay be provided by an API, thereby allowing other computer systemsseparate from the recommendation system to make use of the generatedrecommendations 670. The recommendations 670 also may undergo testingand modification. For example, a test set of data may include 90% ofconsumption and feedback data for a particular user, set of media items,or the like, upon which the models are trained at 640. The remaining 10%may be used to compare known consumption and feedback to the predictiveresults of the model. Other testing techniques may be used.

The use of user-specific pools and the dynamic adjustment of each user'spool based upon feedback received from the user may provide benefitsover conventional media recommendation systems, which typically operateby following acoustic- or genre-specific relationships among all mediaitems available to the system. For example, music recommendation andplayback systems often maintain a tree of acoustic similarities betweenindividual songs and artists. When a user selects a particular song, thesystem typically will select following songs that are acoustically andgenerically similar, without regard for any other classification. Thus,embodiments disclosed herein may provide more precisely tailored mediaplayback experiences for users, while still providing access to mediaitems that the user may not consider similar, but would still enjoyconsuming.

Embodiments disclosed herein also may provide benefit to, andimprovement upon, the underlying computer architecture upon which mediarecommendation and playback systems as disclosed herein are implemented.For example, as previously described, systems and techniques disclosedherein may allow for more efficient and accurate processing of mediafiles, such as more accurate identification of “similar” media items,more accurate recommendations provided to users, and reduced processingspeeds and resource requirements to process the same number of mediaitems. That is, systems according to embodiments disclosed herein mayoperate more efficiently and require fewer computing resources thanconventional recommendation and playback systems. Furthermore,embodiments disclosed herein may provide more precise recommendations tousers than would be achievable using conventional or known techniques.For example, techniques disclosed herein may be more accurate atpredicting media items that a user may wish to consume, based only ontheir previous consumption habits and history, than would otherwise bepossible. In addition, embodiments disclosed herein may be particularlysuited and configured to operate in a computer-networked environment,such as where connections to other systems such as social media systemsmay provide additional sources of data that can be used by systems asdisclosed herein to more accurately predict a user's preferences. Suchpredictions and prediction accuracy would generally not be availableoutside of the computer networked systems disclosed herein.

Although embodiments disclosed herein may be described by way ofexamples relating to music categorization and consumption, it will beapparent to one of skill in the art that the same techniques and systemsmay be used for, or extended to, other types of media such as films,short videos, or the like. For example, films often have one or moreassociated “artists” such as studios, directors, producers, and thelike. Thus, in an embodiment directed solely to film recommendation,such artists may be used in the same manner as described in examplesprovided herein instead of the traditional music “artist.”

Implementations of the presently disclosed subject matter may beimplemented in and used with a variety of component and networkarchitectures. FIG. 1 is an example computer 20 suitable forimplementations of the presently disclosed subject matter. The computer20 includes a bus 21 which interconnects major components of thecomputer 20, such as a central processor 24, a memory 27 (typically RAM,but which may also include ROM, flash RAM, or the like), an input/outputcontroller 28, a user display 22, such as a display screen via a displayadapter, a user input interface 26, which may include one or morecontrollers and associated user input devices such as a keyboard, mouse,and the like, and may be closely coupled to the I/O controller 28, fixedstorage 23, such as a hard drive, flash storage, Fibre Channel network,SAN device, SCSI device, and the like, and a removable media component25 operative to control and receive an optical disk, flash drive, andthe like.

The bus 21 allows data communication between the central processor 24and the memory 27, which may include read-only memory (ROM) or flashmemory (neither shown), and random access memory (RAM) (not shown), aspreviously noted. The RAM is generally the main memory into which theoperating system and application programs are loaded. The ROM or flashmemory can contain, among other code, the Basic Input-Output system(BIOS) which controls basic hardware operation such as the interactionwith peripheral components.

Applications resident with the computer 20 are generally stored on andaccessed via a computer readable medium, such as a hard disk drive(e.g., fixed storage 23), an optical drive, floppy disk, or otherstorage medium 25.

The fixed storage 23 may be integral with the computer 20 or may beseparate and accessed through other interfaces. A network interface 29may provide a direct connection to a remote server via a telephone link,to the Internet via an internet service provider (ISP), or a directconnection to a remote server via a direct network link to the Internetvia a POP (point of presence) or other technique. The network interface29 may provide such connection using wireless techniques, includingdigital cellular telephone connection, Cellular Digital Packet Data(CDPD) connection, digital satellite data connection or the like. Forexample, the network interface 29 may allow the computer to communicatewith other computers via one or more local, wide-area, or othernetworks, as shown in FIG. 2.

Many other devices or components (not shown) may be connected in asimilar manner (e.g., document scanners, digital cameras and so on).Conversely, all of the components shown in FIG. 1 need not be present topractice the present disclosure. The components can be interconnected indifferent ways from that shown. The operation of a computer such as thatshown in FIG. 1 is readily known in the art and is not discussed indetail in this application. Code to implement the present disclosure canbe stored in computer-readable storage media such as one or more of thememory 27, fixed storage 23, removable media 25, or on a remote storagelocation.

FIG. 2 shows an example network arrangement according to animplementation of the disclosed subject matter. One or more clients 10,11, such as local computers, smart phones, tablet computing devices, andthe like may connect to other devices via one or more networks 7. Thenetwork may be a local network, wide-area network, the Internet, or anyother suitable communication network or networks, and may be implementedon any suitable platform including wired and/or wireless networks. Theclients may communicate with one or more servers 13 and/or databases 15.The devices may be directly accessible by the clients 10, 11, or one ormore other devices may provide intermediary access such as where aserver 13 provides access to resources stored in a database 15. Theclients 10, 11 also may access remote platforms 17 or services providedby remote platforms 17 such as cloud computing arrangements andservices. The remote platform 17 may include one or more servers 13and/or databases 15.

More generally, various implementations of the presently disclosedsubject matter may include or be implemented in the form ofcomputer-implemented processes and apparatuses for practicing thoseprocesses. Implementations also may be implemented in the form of acomputer program product having computer program code containinginstructions implemented in non-transitory and/or tangible media, suchas floppy diskettes, CD-ROMs, hard drives, USB (universal serial bus)drives, or any other machine readable storage medium, wherein, when thecomputer program code is loaded into and executed by a computer, thecomputer becomes an apparatus for practicing implementations of thedisclosed subject matter. Implementations also may be implemented in theform of computer program code, for example, whether stored in a storagemedium, loaded into and/or executed by a computer, or transmitted oversome transmission medium, such as over electrical wiring or cabling,through fiber optics, or via electromagnetic radiation, wherein when thecomputer program code is loaded into and executed by a computer, thecomputer becomes an apparatus for practicing implementations of thedisclosed subject matter. When implemented on a general-purposemicroprocessor, the computer program code segments configure themicroprocessor to create specific logic circuits.

In some configurations, a set of computer-readable instructions storedon a computer-readable storage medium may be implemented by ageneral-purpose processor, which may transform the general-purposeprocessor or a device containing the general-purpose processor into aspecial-purpose device configured to implement or carry out theinstructions. Implementations may be implemented using hardware that mayinclude a processor, such as a general purpose microprocessor and/or anApplication Specific Integrated Circuit (ASIC) that implements all orpart of the techniques according to implementations of the disclosedsubject matter in hardware and/or firmware. The processor may be coupledto memory, such as RAM, ROM, flash memory, a hard disk or any otherdevice capable of storing electronic information. The memory may storeinstructions adapted to be executed by the processor to perform thetechniques according to implementations of the disclosed subject matter.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific implementations. However, theillustrative discussions above are not intended to be exhaustive or tolimit implementations of the disclosed subject matter to the preciseforms disclosed. Many modifications and variations are possible in viewof the above teachings. The implementations were chosen and described inorder to explain the principles of implementations of the disclosedsubject matter and their practical applications, to thereby enableothers skilled in the art to utilize those implementations as well asvarious implementations with various modifications as may be suited tothe particular use contemplated.

1-20. (canceled)
 21. A method comprising: calculating, at a processor,distances between vector representations of media items in a first pooland a vector representation of a seed media item, wherein the seed mediaitem is of a type selected from the group consisting of: trending,emerging, recent, liked, selected, active, and recommended; sorting, atthe processor, the first pool based upon the calculated distances;receiving, at the processor, a feedback signal about a first media itemthat is selected to be played based on the sorting of the first pool;and modifying, at the processor and based upon the feedback signal,contents of the first pool to generate a second pool, wherein the secondpool includes a second media item that is not included in the firstpool, and wherein the second media item is selected based upon adistance between a vector representation of the second media item and avector representation of the first media item.
 22. The method of claim21, wherein the first pool comprises a subset of a plurality of mediaitems available within a computerized media recommendation system. 23.The method of claim 21, further comprising: generating, at theprocessor, the vector representations of the media items in the firstpool.
 24. The method of claim 21, further comprising: generating, at theprocessor, the first pool.
 25. The method of claim 21, wherein thegenerating the first pool comprises generating the first pool based onthe seed media item.
 26. The method of claim 21, further comprising:receiving, at the processor, a selection of the seed media item, whereinthe receiving the selection comprises receiving the selection from amonga plurality of seed media items.
 27. The method of claim 26, wherein theplurality of seed media items comprises a first type of seed media itemsand a second type of seed media items, each of the first type of seedmedia items and the second media items is defined by at least one of acount, over a specific duration of time, of a consumption of a mediaitem, or a count, over the specific duration of time, of receipts ofpositive feedback about the media item, the first type of seed mediaitems being different from the second type of seed media items.
 28. Themethod of claim 27, wherein the media item in the count of theconsumption of the media item comprises at least one of a media itemdetermined to be less well-known than an average well-known media item,a media item consumed by a user from a source external to a computerizedmedia recommendation system, or a media item being consumed by usersconnected to the user by a social media network.
 29. The method of claim27, wherein the media item in the count of the receipts of the positivefeedback comprises at least one of a media item associated with anaffirmative action to express the positive feedback or a media itemselected by an individual associated with an entity that controls acomputerized media recommendation system.
 30. The method of claim 21,further comprising: selecting, at the processor, the seed media item.31. The method of claim 30, wherein the seed media item is available forplayback to a user within a computerized media recommendation system.32. The method of claim 30, wherein the selecting comprises selecting,based on a profile vector of a user, the seed media item.
 33. The methodof claim 32, wherein the selecting the seed media item the profilevector comprises a normalized sum of vector representations of mediaitems previously consumed by the user.
 34. The method of claim 33,wherein the profile vector is updated in real time.
 35. The method ofclaim 21, wherein the second pool excludes a third media item, the thirdmedia item being in the first pool.